Compute study guide
Here’s a study guide covering key AWS compute services to help you understand and compare them:
AWS Compute Services Study Guide
1️⃣ Serverless Computing ⚡ – AWS Lambda
✔️ What it is: A serverless compute service that runs code without provisioning or managing servers.
✔️ Use cases: Event-driven applications, automation, backend processing.
✔️ Key features:
- Executes code in response to events (e.g., API calls, file uploads).
- Auto-scales based on demand.
- Supports multiple programming languages.
🔎 Study Tip: Learn how to trigger Lambda functions using API Gateway, S3, and DynamoDB.
2️⃣ Virtual Servers 🖥️ – Amazon EC2
✔️ What it is: Provides scalable virtual machines to run applications in the cloud.
✔️ Use cases: Hosting websites, databases, enterprise applications.
✔️ Key features:
- Offers customizable instance types for different workloads.
- Supports auto-scaling and load balancing.
- Integrates with EBS for persistent storage.
🔎 Study Tip: Understand EC2 instance types, pricing models, and security configurations.
3️⃣ Container Management 🚢 – Amazon ECS & EKS
✔️ Amazon ECS (Elastic Container Service) → Fully managed container orchestration.
✔️ Amazon EKS (Elastic Kubernetes Service) → Managed Kubernetes clusters on AWS.
✔️ Use cases: Microservices, containerized applications, DevOps workflows.
✔️ Key features:
- ECS integrates with AWS Fargate for serverless containers.
- EKS simplifies Kubernetes cluster management.
- Supports auto-scaling and networking configurations.
🔎 Study Tip: Compare ECS vs EKS vs Fargate to understand when to use each service.
4️⃣ Elastic Compute 📈 – Amazon EC2 Auto Scaling
✔️ What it is: Automatically scales EC2 instances based on demand.
✔️ Use cases: High-traffic applications, dynamic workloads.
✔️ Key features:
- Monitors CPU, memory, and network usage to adjust resources.
- Ensures high availability by replacing unhealthy instances.
- Works with Elastic Load Balancing for traffic distribution.
🔎 Study Tip: Learn how to configure Auto Scaling Groups and policies for cost optimization.
5️⃣ Batch Processing 💼 – AWS Batch
✔️ What it is: A fully managed batch computing service for large-scale workloads.
✔️ Use cases: Machine learning training, simulations, financial modeling.
✔️ Key features:
- Automatically provisions compute resources based on job requirements.
- Supports containerized workloads using ECS and EKS.
- Optimizes costs by using Spot Instances for batch jobs.
🔎 Study Tip: Understand job definitions, queues, and compute environments in AWS Batch.
AWS Amazon EC2 (Elastic Compute Cloud) provides secure and resizable compute capacity in the cloud, allowing users to run virtual servers for various workloads.
Key Features of Amazon EC2
✔️ Scalable Virtual Machines → Choose from different instance types based on workload needs.
✔️ Flexible Compute Options → Supports Intel, AMD, and Arm processors.
✔️ Auto Scaling & Load Balancing → Adjusts resources dynamically based on demand.
✔️ Security & Compliance → Built-in encryption and AWS Nitro System for secure compute.
✔️ Cost Optimization → Offers Spot Instances, Savings Plans, and Reserved Instances for cost-effective computing.
Use Cases
🔹 Web Hosting → Deploy websites and applications with scalable infrastructure.
🔹 Machine Learning & AI → Train and deploy ML models efficiently.
🔹 High-Performance Computing (HPC) → Run simulations, analytics, and large-scale computations.
🔹 Enterprise Applications → Host databases, ERP systems, and business applications.
Would you like guidance on choosing the right EC2 instance type for your workload? 🚀
You can explore more details here.
AWS Lambda is the service designed to run code without provisioning or managing infrastructure. It is a serverless compute service that automatically scales and executes code in response to events.
Key Features of AWS Lambda
✔️ No server management → AWS handles provisioning, scaling, and maintenance.
✔️ Event-driven execution → Runs code in response to triggers like API calls, file uploads, or database changes.
✔️ Automatic scaling → Adjusts resources dynamically based on demand.
✔️ Pay-per-use pricing → Charges only for the compute time used.
Use Cases
🔹 Microservices → Build modular applications without managing infrastructure.
🔹 Data Processing → Handle real-time data streams from sources like Amazon S3 or Kinesis.
🔹 API Backends → Power RESTful APIs with AWS API Gateway.
🔹 Automation & Scheduled Tasks → Run scheduled jobs without maintaining servers.
You can explore more details here. Let me know if you need help setting it up! 🚀
AWS Auto Scaling automatically provisions the optimal amount of compute capacity based on demand. It ensures that applications have the right resources at the right time, improving performance and cost efficiency.
Key Features of AWS Auto Scaling
✔️ Dynamic Scaling → Adjusts compute resources automatically based on traffic patterns.
✔️ Predictive Scaling → Uses machine learning to anticipate demand spikes and scale ahead of time.
✔️ Multi-Service Support → Works with EC2, ECS, EKS, DynamoDB, and Aurora.
✔️ Cost Optimization → Prevents over-provisioning by scaling down when demand decreases.
Use Cases
🔹 Web Applications → Ensures high availability during peak traffic.
🔹 Batch Processing → Allocates compute resources efficiently for large-scale jobs.
🔹 Machine Learning & Analytics → Scales infrastructure dynamically for data-intensive workloads.
Would you like guidance on setting up Auto Scaling policies for your environment? 🚀
You can explore more details here.
AWS Batch is the service designed to run and manage batch computing jobs at any scale. It enables developers, scientists, and engineers to efficiently execute hundreds of thousands of batch jobs without managing infrastructure manually.
Key Features of AWS Batch
✔️ Automatic Compute Provisioning → Dynamically allocates the optimal compute resources based on job requirements.
✔️ Integration with AWS Services → Works with Amazon ECS, EKS, and AWS Fargate for containerized workloads.
✔️ Job Scheduling & Dependencies → Supports multi-node parallel jobs and job queues for efficient execution.
✔️ Cost Optimization → Uses Spot Instances to reduce costs for large-scale batch processing.
Use Cases
🔹 Scientific Simulations → Run large-scale computations for research and analytics.
🔹 Machine Learning Training → Process massive datasets efficiently.
🔹 Financial Modeling → Execute complex calculations for risk analysis.
🔹 Rendering & Media Processing → Automate video encoding and image processing tasks.
Would you like guidance on setting up AWS Batch job definitions or optimizing compute environments? 🚀
You can explore more details here.
To quickly deploy and manage a web application using Platform as a Service (PaaS) on AWS, you can use AWS Elastic Beanstalk or AWS App Runner.
1️⃣ AWS Elastic Beanstalk
✔️ Fully managed PaaS → Deploy applications without managing infrastructure.
✔️ Supports multiple languages → Java, .NET, Python, Node.js, PHP, Ruby, Go, and Docker.
✔️ Automatic scaling & monitoring → Handles load balancing, health checks, and scaling.
✔️ Easy deployment → Upload your code, and AWS provisions the necessary resources.
🔎 Study Tip: Learn how to deploy applications using Elastic Beanstalk CLI and configure environments.
2️⃣ AWS App Runner
✔️ Deploy directly from source code or container images.
✔️ Automatic scaling & load balancing → No need to configure servers.
✔️ Ideal for microservices & containerized applications.
✔️ Simplifies CI/CD workflows → Integrates with GitHub and Amazon ECR.
🔎 Study Tip: Explore how App Runner compares to Elastic Beanstalk for different workloads.
Quick Deployment Steps (Elastic Beanstalk Example)
1️⃣ Install the Elastic Beanstalk CLI:
bash
pip install awsebcli --upgrade
2️⃣ Initialize your project:
bash
eb init -p python-3.8 my-app
3️⃣ Create an environment and deploy:
bash
eb create my-app-env
eb deploy
Your web application will be live with auto-scaling, monitoring, and managed infrastructure! 🚀
Would you like help choosing between Elastic Beanstalk vs App Runner for your project?
You can explore more details here and here.
Final Study Tips
✅ Compare EC2 vs Lambda vs Fargate – Know when to use each.
✅ Learn IAM roles & security best practices for compute services.
✅ Explore cost optimization strategies for running workloads efficiently.